COMP_SCI 496: AI Perspectives: Symbolic Reasoning to Deep Learning Computer Science Northwestern Engineering

As you can easily imagine, this is a very heavy and time-consuming job as there are many many ways of asking or formulating the same question. Since its foundation as an academic discipline in 1955, Artificial Intelligence (AI) research field has been divided into different camps, of which symbolic AI and machine learning. While symbolic AI used to dominate in the first decades, machine learning has been very trendy lately, so let’s try to understand each of these approaches and their main differences when applied to Natural Language Processing (NLP). Insofar as computers suffered from the same chokepoints, their builders relied on all-too-human hacks like symbols to sidestep the limits to processing, storage and I/O. As computational capacities grow, the way we digitize and process our analog reality can also expand, until we are juggling billion-parameter tensors instead of seven-character strings. At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research.

At birth, the newborn possesses limited innate knowledge about our world. A newborn does not know what a car is, what a tree is, or what happens if you freeze water. The newborn does not understand the meaning of the colors in a traffic light system or that a red heart is the symbol of love. A newborn starts only with sensory abilities, the ability to see, smell, taste, touch, and hear.

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Examples of common-sense reasoning include implicit reasoning about how people think or general knowledge of day-to-day events, objects, and living creatures. This kind of knowledge is taken for granted and not viewed as noteworthy. Just like deep learning was waiting for data and computing to catch up with its ideas, so has symbolic AI been waiting for neural networks to mature. And now that two complementary technologies are ready to be synched, the industry could be in for another disruption — and things are moving fast. You could achieve a similar result to that of a neuro-symbolic system solely using neural networks, but the training data would have to be immense. Moreover, there’s always the risk that outlier cases, for which there is little or no training data, are answered poorly.

This spawns the apocryphal story about the CIA translating The spirit is willing, but the flesh is weak into Russian and back into English, resulting in The vodka is good, but the meat is rotten. To apply legal reasoning, a judge must identify the facts of a case, the question, the relevant legislation and any precedents (in common law jurisdictions). A judge uses legal reasoning to reach a logical conclusion, such as deciding whether a defendant is guilty or not. Deductive reasoning is deducing new information from logically related known information.

Measuring progress in Symbolic AI: the biggest surprise in AI trends report from Stanford

“With symbolic AI there was always a question mark about how to get the symbols,” IBM’s Cox said. The world is presented to applications that use symbolic AI as images, video and natural language, which is not the same as symbols. He is a long-standing researcher in Knowledge Representation and Reasoning (KR&R), and is the past President of KR. His recent research includes using KR&R to tasks in vision and languages, thus combining symbolic and neural approaches.

They are our statement’s primary subjects and the components we must model our logic around. This step is vital for us to understand the different components of our world correctly. Our target for this process is to define a set of predicates that we can evaluate to be either TRUE or FALSE. This target requires that we also define the syntax and semantics of our domain through predicate logic. The Second World War saw massive scientific contributions and technological advancements.

In contrast, a neural network may be right most of the time, but when it’s wrong, it’s not always apparent what factors caused it to generate a bad answer. And finding important correlations in the data is also important, useful and has plenty of useful applications. But cognition, and specifically human-level cognition, is a lot more then observing some pattern in the data. Not withstanding all the misguided hype and all the media frenzy, no plausible theory has so far been able to demonstrate that the kind of high-level reasoning humans are capable of doing can escape symbolic reasoning. Symbolic AI is a sub-field of artificial intelligence that focuses on the high-level symbolic (human-readable) representation of problems, logic, and search. For instance, if you ask yourself, with the Symbolic AI paradigm in mind, “What is an apple?

But if we add axioms which ci

umscribe the abnormality predicate to [newline]which they are currently known say “Bird

Tweety” then the inference can be drawn. If these two sets of premises are

satisfied, then the rule states that we can conclude that John owns a car. The rule is only accessed if we [newline]wish to know whether or not John owns a car then an answer can not be deduced

from our current beliefs.

Enabling machine intelligence through symbols

How to explain the input-output behavior, or even inner activation states, of deep learning networks is a highly important line of investigation, as the black-box character of existing systems hides system biases and generally fails to provide a rationale for decisions. Recently, awareness is growing that explanations should not only rely on raw system inputs but should reflect background knowledge. Another way the two AI paradigms can be combined is by using neural networks to help prioritize how symbolic programs organize and search through multiple facts related to a question. For example, if an AI is trying to decide if a given statement is true, a symbolic algorithm needs to consider whether thousands of combinations of facts are relevant.

There are a number of ways of representing real-world events and their effects in logical languages such as Prolog, such as the Event Calculus, which is a logical way of representing events and their effects developed by Robert Kowalski and Marek Sergot in 1986. In Germany in 2017, Bernhard Waltl and other researchers at the Technical University of Munich trained a machine learning classifier on 5990 tax law appeals to use 11 features to predict the outcome of a new tax appeal. Today, nobody would dream of building a computerised translation system with symbolic AI. Monotonic reasoning is used in conventional reasoning systems, and a logic-based system is monotonic. Abductive reasoning is a form of logical reasoning which starts with single or multiple observations then seeks to find the most likely explanation or conclusion for the observation.

The qualitative measures are efficiency, reliability, productivity, and robustness whereas quantitative measures include time, multi-criteria optimization, and resources such as mass and so on. Summarizing, neuro-symbolic artificial intelligence is an emerging subfield of AI that promises to favorably combine knowledge representation and deep learning in order to improve deep learning and to explain outputs of deep-learning-based systems. Neuro-symbolic approaches carry the promise that they will be useful for addressing complex AI problems that cannot be solved by purely symbolic or neural means. We have laid out some of the most important currently investigated research directions, and provided literature pointers suitable as entry points to an in-depth study of the current state of the art.

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